无轴传动凹版印刷机的相邻偏差耦合同步控制

针对无轴传动凹版印刷机的同步协调控制,考虑印刷基体张力恒定和套印准确的要求,基于相邻偏差耦合思想提出了一种能同时满足凹版印刷机无轴传动系统完全同步和比例同步特点的控制结构。针对无轴传动系统动力驱动部件非线性、强耦合的特点,设计了2阶自抗扰控制器以实现收放卷牵引单元与印刷色组单元高精度的跟踪控制和扰动补偿。构建了无轴传动凹版印刷机同步控制策略的仿真模型,仿真结果表明,提出的同步控制结构具有较高的同步控制精度和稳定性,设计的自抗扰控制器具有较好的速度跟踪性能和抗扰能力。     

仅利用位置测量信息的机械手鲁棒滑模跟踪控制

针对机械手的鲁棒控制问题 ,提出了一种仅利用位置测量信息的机械手鲁棒滑模跟踪控制器设计方案。通过引入滑模观测器 ,避免了对关节角速度信号的测量 ,该控制器不仅能保证闭环系统所有信号全局有界 ,且能使跟踪误差以指数速度收敛到零的小邻域内。计算机仿真验证了该控制方案的可行性和有效性     

Distributed robust adaptive control of high order nonlinear multi agent systems

In this paper, a robust adaptive neural network based controller is presented for multi agent high order nonlinear systems with unknown nonlinear functions, unknown control gains and unknown actuator failures. At first, Neural Network (NN) is used to approximate the nonlinear uncertainty terms derived from the controller design procedure for the followers. Then, a novel distributed robust adaptive controller is developed by combining the backstepping method and the Dynamic Surface Control (DSC) approach. The proposed controllers are distributed in the sense that the designed controller for each follower agent only requires relative state information between itself and its neighbors. By using the Young's inequality, only few parameters need to be tuned regardless of NN nodes number. Accordingly, the problems of dimensionality curse and explosion of complexity are counteracted, simultaneously. New adaptive laws are designed by choosing the appropriate Lyapunov-Krasovskii functionals. The proposed approach proves the boundedness of all the closed-loop signals in addition to the convergence of the distributed tracking errors to a small neighborhood of the origin. Simulation results indicate that the proposed controller is effective and robust.     

Mean deviation coupling synchronous control for multiple motors via second-order adaptive sliding mode control

A new mean deviation coupling synchronization control strategy is developed for multiple motor control systems, which can guarantee the synchronization performance of multiple motor control systems and reduce complexity of the control structure with the increasing number of motors. The mean deviation coupling synchronization control architecture combining second-order adaptive sliding mode control (SOASMC) approach is proposed, which can improve synchronization control precision of multiple motor control systems and make speed tracking errors, mean speed errors of each motor and speed synchronization errors converge to zero rapidly. The proposed control scheme is robustness to parameter variations and random external disturbances and can alleviate the chattering phenomena. Moreover, an adaptive law is employed to estimate the unknown bound of uncertainty, which is obtained in the sense of Lyapunov stability theorem to minimize the control effort. Performance comparisons with master–slave control, relative coupling control, ring coupling control, conventional PI control and SMC are investigated on a four-motor synchronization control system. Extensive comparative results are given to shown the good performance of the proposed control scheme.     

基于神经网络的流涎机组多电机同步控制研究

针对流涎薄膜生产机组多电机同步控制系统的非线性、时变、容易受负载扰动等特性,提出了基于神经网络PID控制器与偏差耦合控制结构相结合的多电机同步控制策略,设计了神经网络PID控制器,并进行了仿真和实验。结果表明：该控制算法稳定性能高、鲁棒性能好、收敛速度快,能够有效克服外部扰动和参数变化带来的同步误差,相对于传统的同步控制方案能够更好地实现流涎机组的多电机同步控制。     

Robust integral of neural network and precision motion control of electrical–optical gyro-stabilized platform with unknown input dead-zones

Parametric uncertainty associated with unmodeled disturbance always exist in physical electrical–optical gyro-stabilized platform systems, and poses great challenges to the controller design. Moreover, the existence of actuator deadzone nonlinearity makes the situation more complicated. By constructing a smooth dead-zone inverse, the control law consisting of the robust integral of a neural network (NN) output plus sign of the tracking error feedback is proposed, in which adaptive law is synthesized to handle parametric uncertainty and RISE robust term to attenuate unmodeled disturbance. In order to reduce the measure noise, a desired compensation method is utilized in controller design, in which the model compensation term depends on the reference signal only. By mainly activating an auxiliary robust control component for pulling back the transient escaped from the neural active region, a multi-switching robust neuro adaptive controller in the neural approximation domain, which can achieve globally uniformly ultimately bounded (GUUB) tracking stability of servo systems recently. An asymptotic tracking performance in the presence of unknown dead-zone, parametric uncertainties and various disturbances, which is vital for high accuracy tracking, is achieved by the proposed robust adaptive backstepping controller. Extensively comparative experimental results are obtained to verify the effectiveness of the proposed control strategy.     

Nonlinear robust control of hypersonic aircrafts with interactions between flight dynamics and propulsion systems

This paper addresses the nonlinear robust tracking controller design problem for hypersonic vehicles. This problem is challenging due to strong coupling between the aerodynamics and the propulsion system, and the uncertainties involved in the vehicle dynamics including parametric uncertainties, unmodeled model uncertainties, and external disturbances. By utilizing the feedback linearization technique, a linear tracking error system is established with prescribed references. For the linear model, a robust controller is proposed based on the signal compensation theory to guarantee that the tracking error dynamics is robustly stable. Numerical simulation results are given to show the advantages of the proposed nonlinear robust control method, compared to the robust loop-shaping control approach.     

Robust adaptive integral backstepping control for opto-electronic tracking system based on modified LuGre friction model

This paper presents a robust adaptive integral backstepping control strategy with friction compensation for realizing accurate and stable control of opto-electronic tracking system in the presence of nonlinear friction and external disturbance. With the help of integral control term to decrease the steady-state error of the system and combining robust adaptive control approach with the backstepping design method, a novel control method is constructed. Nonlinear modified LuGre observer is designed to estimate friction behavior. Robust adaptive integral backstepping control strategy is developed to compensate the changes in friction behavior and external disturbance of the servo system. The stability of the opto-electronic tracking system is proved by Lyapunov criterion. The performance of robust adaptive integral backstepping controller is verified by the opto-electronic tracking system with modified LuGre model in simulation and practical experiments. Compared to the adaptive integral backstepping sliding mode control method, the root mean square of angle error is reduced by 26.6% when the proposed control method is used. The experiment results demonstrate the effectiveness and robustness of the proposed strategy.     

基于零相位误差跟踪控制器的轮廓误差交叉耦合控制

针对高精密轮廓加工，在分析系统轮廓误差的基础上，通过减小跟踪误差来间接减小轮廓误差，提出了将交叉耦合控制器和零相位误差跟踪控制器相结合的控制策略。交叉耦合控制器用以增加各轴之间的匹配程度，以减小轮廓误差；零相位误差跟踪控制器作为前馈跟踪控制器，提高了快速性，使系统实现准确跟踪。仿真结果表明所提出的控制方案是有效的，能达到良好的轮廓跟踪效果，从而提高了轮廓加工精度。     

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations.     

Sliding-mode repetitive learning control with integral sliding-mode perturbation compensation

This paper proposes a sliding-mode repetitive learning control (SMRLC) scheme with an integral sliding-mode perturbation observer (ISMPO) for repetitive tracking control tasks. The three control strategies which are synthesized to yield excellent tracking performance are: (1) the pole-placement feedback control to specify the desired error dynamics; (2) ISMPO-based feedback compensation as the robust part; and (3) a feedforward learning component that refines the control to improve system performance through repetitive trials. The ISMPO-based feedback compensation ensures that there is only small tracking error during initial learning trials and enhances system insensitivity to exceptional and aperiodic disturbances. The feedforward learning compensation is updated according to a certain switching signal that is equivalent to the compensation error of the feedforward control, yielding fast convergence of the learning process from trial to trial. Experimental results demonstrate the feasibility of the proposed scheme.     

Adaptive control of 5 DOF upper-limb exoskeleton robot with improved safety

This paper studies an adaptive control strategy for a class of 5 DOF upper-limb exoskeleton robot with a special safety consideration. The safety requirement plays a critical role in the clinical treatment when assisting patients with shoulder, elbow and wrist joint movements. With the objective of assuring the tracking performance of the pre-specified operations, the proposed adaptive controller is firstly designed to be robust to the model uncertainties. To further improve the safety and fault-tolerance in the presence of unknown large parameter variances or even actuator faults, the adaptive controller is on-line updated according to the information provided by an adaptive observer without additional sensors. An output tracking performance is well achieved with a tunable error bound. The experimental example also verifies the effectiveness of the proposed control scheme.     

基于广义预测相邻耦合理论的多点顶推系统同步控制策略

针对多点顶推系统的同步控制问题,提出了一种广义预测相邻耦合同步控制策略。根据多变量广义预测理论,建立了多缸电液伺服系统的CARIMA模型。结合相邻耦合控制理论,构造了同步误差观测器,预测了同步误差。对广义预测控制算法进行了改进,在性能指标中嵌入跟踪误差与同步误差及其差分量,确保同步误差与跟踪误差在全局范围内并渐进收敛于零。实验表明该策略较之主从式PID控制策略,具有更好的跟踪性能和同步性能。     

Robust iterative learning contouring controller with disturbance observer for machine tool feed drives

In feed drive systems, particularly machine tools, a contour error is more significant than the individual axial tracking errors from the view point of enhancing precision in manufacturing and production systems. The contour error must be within the permissible tolerance of given products. In machining complex or sharp-corner products, large contour errors occur mainly owing to discontinuous trajectories and the existence of nonlinear uncertainties. Therefore, it is indispensable to design robust controllers that can enhance the tracking ability of feed drive systems. In this study, an iterative learning contouring controller consisting of a classical Proportional-Derivative (PD) controller and disturbance observer is proposed. The proposed controller was evaluated experimentally by using a typical sharp-corner trajectory, and its performance was compared with that of conventional controllers. The results revealed that the maximum contour error can be reduced by about 37% on average.     

Robust disturbance rejection control of a biped robotic system using high-order extended state observer

This study addressed the problem of robust control of a biped robot based on disturbance estimation. Active disturbance rejection control was the paradigm used for controlling the biped robot by direct active estimation. A robust controller was developed to implement disturbance cancelation based on a linear extended state observer of high gain class. A robust high-gain scheme was proposed for developing a state estimator of the biped robot despite poor knowledge of the plant and the presence of uncertainties. The estimated states provided by the state estimator were used to implement a feedback controller that was effective in actively rejecting the perturbations as well as forcing the trajectory tracking error to within a small vicinity of the origin. The theoretical convergence of the tracking error was proven using the Lyapunov theory. The controller was implemented by numerical simulations that showed the convergence of the tracking error. A comparison with a high-order sliding-mode-observer-based controller confirmed the superior performance of the controller using the robust observer introduced in this study. Finally, the proposed controller was implemented on an actual biped robot using an embedded hardware-in-the-loop strategy.     

Fractional active disturbance rejection control

A fractional active disturbance rejection control (FADRC) scheme is proposed to improve the performance of commensurate linear fractional order systems (FOS) and the robust analysis shows that the controller is also applicable to incommensurate linear FOS control. In FADRC, the traditional extended states observer (ESO) is generalized to a fractional order extended states observer (FESO) by using the fractional calculus, and the tracking differentiator plus nonlinear state error feedback are replaced by a fractional proportional-derivative controller. To simplify controller tuning, the linear bandwidth-parameterization method has been adopted. The impacts of the observer bandwidth ω_o and controller bandwidth ω_c on system performance are then analyzed. Finally, the FADRC stability and frequency-domain characteristics for linear single-input single-output FOS are analyzed. Simulation results by FADRC and ADRC on typical FOS are compared to demonstrate the superiority and effectiveness of the proposed scheme.     

Adaptive neural network based position tracking control for Dual-master/Single-slave teleoperation system under communication constant time delays

The novel trajectory tracking control strategies for trilateral teleoperation systems with Dual-master/Single-slave robot manipulators under communication constant time delays are proposed in this article. By incorporating this design technique into the neural network (NN) based adaptive control framework, two controllers are designed for the trilateral teleoperation systems in free motion. First, with acceleration measurements, an adaptive controller under the synchronization variables containing the position and velocity error is constructed to guarantee the position and velocity tracking errors between the trilateral teleoperation systems asymptotically converge to zero. Second, without acceleration measurements, an adaptive controller under the new synchronization variables is presented such that the trilateral teleoperation systems can obtain the same trajectory tracking performance as the first controller. Third, in term of establishing suitable Lyapunov–Krasovskii functionals, the asymptotic tracking performances of the trilateral teleoperation systems can be derived independent of the communication constant time delays. Moreover, these two controllers are obtained without the knowledge of upper bounds of the NN approximation errors, respectively. Finally, simulation results are presented to demonstrate the validity of the proposed methods.     

Adaptive super-twisting sliding mode observer based robust backstepping sensorless speed control for IPMSM

This paper proposes a higher-order sliding mode observer based robust backstepping control to realize high-performance sensorless speed regulation for the interior permanent magnet synchronous motor (IPMSM). A new robust adaptive super-twisting higher-order sliding mode based observer is proposed to estimate the rotor position. The proposed observer has advantages of sliding chattering reduction and robustness against uncertainties. And, a new robust integral adaptive backstepping control with sliding mode actions is designed to achieve precise speed regulation. The uncertainties with unknown bounds can be stabilized by the sliding mode actions. And both transient and steady performance can be achieved by using the sliding mode and integral actions simultaneously. Then, a sensorless scheme is put forward to by combining the presented observer and the proposed controller. The stability of the observer and controller are verified. Simulation and experiment results validate the proposed approach.     

电动转向控制系统跟踪性能研究

电动转向系统依靠助力电动机实现转向助力,控制系统的跟踪性能是影响电动转向系统助力性能的重要因素,较差的跟踪性能将会产生转向助力滞后现象,使驾驶路感变差。助力转矩偏差直接影响到转向系统的跟踪性能,影响助力转矩偏差的因素有转向盘输入转矩和转向轴转矩测量噪声,抑制转向盘输入转矩和转向轴转矩测量噪声引起的电动机助力转矩偏差是提高电动转向系统跟踪性能的有效手段。将H_∞控制理论应用于电动转向控制系统跟踪性能的研究,建立了电动转向系统数学模型,采用线性矩阵不等式处理方法设计了最优H_∞控制器,应用Matlab软件进行了计算机仿真,并根据最优H_∞控制器编制了电动转向系统控制程序,进行了台架试验。仿真和试验结果表明,所设计的电动转向控制系统具有较好的跟踪性能。     

Dynamics Modeling and Robust Trajectory Tracking Control for a Class of Hybrid Humanoid Arm Based on Neural Network

In order to solve the problem of trajectory tracking for a class of novel serial-parallel hybrid humanoid arm(HHA), which has parameters uncertainty, frictions, disturbance, abrasion and pulse forces derived from motors, a multistep dynamics modeling strategy is proposed and a robust controller based on neural network(NN)-adaptive algorithm is designed. At the first step of dynamics modeling, the dynamics model of the reduced HHA is established by Lagrange method. At the second step of dynamics modeling, the parameter uncertain part resulting mainly from the idealization of the HHA is learned by adaptive algorithm. In the trajectory tracking controller, the radial basis function(RBF) NN, whose optimal weights are learned online by adaptive algorithm, is used to learn the upper limit function of the total uncertainties including frictions, disturbances, abrasion and pulse forces. To a great extent, the conservatism of this robust trajectory tracking controller is reduced, and by this controller the HHA can impersonate mostly human actions. The proof and simulation results testify the validity of the adaptive strategy for parameter learning and the neural network-adaptive strategy for the trajectory tracking control.